Li Guangqian: Improve the big data management system to help epidemic prevention and control


2022-01-14: [Article Link]  Lee Guangcheng: A sound big data management system to help prevent and control the epidemic For more than two years now, our e-government and big data decision support systems have effectively underpinned the defence and control of Covid-19 Pandemic in our country, and have done much to achieve the dynamic goal of zero. In the process, however, there have also been a number of accidents of various sizes. For example, the recent crash of the “one-size-fits-all” system in Xianan, and the short-time visitation of the Cantonese Quantico code, have been the subject of widespread social concern. Many discuss the causes of accidents at the technical level, arguing that the main problem is that the servers are overloaded at short notice. Indeed, this is only the case at the technical level, and the failure of such e-government and big data management systems reflects deep-seated problems that deserve our in-depth analysis and in-depth analysis. Currently, our e-government and big data management systems continue to be developed on the basis of traditional e-government construction, the greatest feature of which is to meet the needs of our own operations. There is no doubt that e-government systems are the first to serve, to be subordinated to the region, and to the business development needs of the sector. In terms of outbreak control, every region has built its own quarantine health code, such as the Beijing Health Treasure, the Guangdong Park Code, and the Chongqing Consort Code. Health services such as the National Health Council (CNSS) promote the Internet + Health Care (IHH) initiative by introducing the "Inhabitants' Electronic Health Code" and requiring local “one-size-fits-all” services. In addition, the State Council clients rely on their major operators to implement a “big-data-trading card” throughout the country. However, given the actual situation in the various regions, none of the above-mentioned codes (cards) can be used universally. Overcoming the many flaws that traditional e-government and big data management systems have brought to the epidemic’s prevention and management requires further strengthening of top-level design. Indeed, at present, top-level design is being emphasized in every sector and place, but the real top-level design should be based on top-level design at the national level. Using the “one-size-fits-all” approach in the area of health care as an example, planning can be better served by placing it at the top of the design of national e-government and big data management. At the heart of top-level design is the establishment of government data resource management systems at the national level, and government data management is increasingly becoming an independent government function and specialized and professional business. With government data at its core, the government data management system combines government functions with government processing, government data sharing, government data openness, data governance, data centre construction, privacy protection, etc., to become a much more professional and complex system for the State and government. This imposes three basic requirements, the first of which is the establishment of scientifically sound government data structures. This requires a scientific classification of big government data based on governance characteristics and data resource management attributes, and an efficient and rational regulatory framework. In terms of national outbreak control, we are required to share basic data resources, such as natural persons, legal persons, natural resources and spatial geography, and medical and health care, at the level of national master data management. This was followed by the establishment of an independent governmental data resource management function, and the Party's 19th plenary session introduced the data production factor theory, and the integrated management of data resources across society is bound to become a new governmental function through the establishment of a relatively independent data resource integrated management agency at the national level. This requires government functions (e.g. the health sector) to separate their data-building management operations and to consolidate and integrate them into the Government's Integrated Data Resources Management Agency. At present, many places have established their own big data management bodies, which is a good trend, but there are still many shortcomings: on the one hand, the local big data authorities are limited in their functions and are understaffed and underresourced, and on the other hand, they lack integrated guidance from the national level, as they do not yet have similar integrated management structures at the national level. The third is the integrated development of the data infrastructure, the main elements of which are the rational layout of the national integrated government data centre and the local government data resource base. This requires us to fully apply the new generation of information technologies, such as cloud computing and artificial intelligence, to support a robust technological and hardware infrastructure for disease prevention and control everywhere. In this sense, at least at the local and municipal levels, there is no need to spend a great deal of money on building its own “one-size-fits-all” disease prevention and control system, but only on end-users and application maintenance of the national disease prevention and control system. At present, the more contagious Omicron strain has invaded the country, which poses even more serious challenges to the prevention and control of the epidemic and places greater demands on the development of our e-government and big data management systems. Two years of epidemic prevention and control practice have also inspired us that only by better promoting top-level design and planning for the development of our disease prevention and control system can we effectively avoid a recurrence of systemic collapse and respond more vigorously to the challenges of disease prevention and control.


Note: This is a translated version of the Chinese news media article. A mature and nuanced reading is suggested.






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Li Guangqian: Improve the big data management system to help epidemic prevention and control


2022-01-14: [Article Link]  Lee Guangcheng: A sound big data management system to help prevent and control the epidemic For more than two years now, our e-government and big data decision support systems have effectively underpinned the defence and control of Covid-19 Pandemic in our country, and have done much to achieve the dynamic goal of zero. In the process, however, there have also been a number of accidents of various sizes. For example, the recent crash of the “one-size-fits-all” system in Xianan, and the short-time visitation of the Cantonese Quantico code, have been the subject of widespread social concern. Many discuss the causes of accidents at the technical level, arguing that the main problem is that the servers are overloaded at short notice. Indeed, this is only the case at the technical level, and the failure of such e-government and big data management systems reflects deep-seated problems that deserve our in-depth analysis and in-depth analysis. Currently, our e-government and big data management systems continue to be developed on the basis of traditional e-government construction, the greatest feature of which is to meet the needs of our own operations. There is no doubt that e-government systems are the first to serve, to be subordinated to the region, and to the business development needs of the sector. In terms of outbreak control, every region has built its own quarantine health code, such as the Beijing Health Treasure, the Guangdong Park Code, and the Chongqing Consort Code. Health services such as the National Health Council (CNSS) promote the Internet + Health Care (IHH) initiative by introducing the "Inhabitants' Electronic Health Code" and requiring local “one-size-fits-all” services. In addition, the State Council clients rely on their major operators to implement a “big-data-trading card” throughout the country. However, given the actual situation in the various regions, none of the above-mentioned codes (cards) can be used universally. Overcoming the many flaws that traditional e-government and big data management systems have brought to the epidemic’s prevention and management requires further strengthening of top-level design. Indeed, at present, top-level design is being emphasized in every sector and place, but the real top-level design should be based on top-level design at the national level. Using the “one-size-fits-all” approach in the area of health care as an example, planning can be better served by placing it at the top of the design of national e-government and big data management. At the heart of top-level design is the establishment of government data resource management systems at the national level, and government data management is increasingly becoming an independent government function and specialized and professional business. With government data at its core, the government data management system combines government functions with government processing, government data sharing, government data openness, data governance, data centre construction, privacy protection, etc., to become a much more professional and complex system for the State and government. This imposes three basic requirements, the first of which is the establishment of scientifically sound government data structures. This requires a scientific classification of big government data based on governance characteristics and data resource management attributes, and an efficient and rational regulatory framework. In terms of national outbreak control, we are required to share basic data resources, such as natural persons, legal persons, natural resources and spatial geography, and medical and health care, at the level of national master data management. This was followed by the establishment of an independent governmental data resource management function, and the Party's 19th plenary session introduced the data production factor theory, and the integrated management of data resources across society is bound to become a new governmental function through the establishment of a relatively independent data resource integrated management agency at the national level. This requires government functions (e.g. the health sector) to separate their data-building management operations and to consolidate and integrate them into the Government's Integrated Data Resources Management Agency. At present, many places have established their own big data management bodies, which is a good trend, but there are still many shortcomings: on the one hand, the local big data authorities are limited in their functions and are understaffed and underresourced, and on the other hand, they lack integrated guidance from the national level, as they do not yet have similar integrated management structures at the national level. The third is the integrated development of the data infrastructure, the main elements of which are the rational layout of the national integrated government data centre and the local government data resource base. This requires us to fully apply the new generation of information technologies, such as cloud computing and artificial intelligence, to support a robust technological and hardware infrastructure for disease prevention and control everywhere. In this sense, at least at the local and municipal levels, there is no need to spend a great deal of money on building its own “one-size-fits-all” disease prevention and control system, but only on end-users and application maintenance of the national disease prevention and control system. At present, the more contagious Omicron strain has invaded the country, which poses even more serious challenges to the prevention and control of the epidemic and places greater demands on the development of our e-government and big data management systems. Two years of epidemic prevention and control practice have also inspired us that only by better promoting top-level design and planning for the development of our disease prevention and control system can we effectively avoid a recurrence of systemic collapse and respond more vigorously to the challenges of disease prevention and control.

Note: This is a translated version of the Chinese news media article. A mature and nuanced reading is suggested.

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